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Building James Stone’s Elkhorn Distillery

2020· book-chapter· en· W4238742552 on OpenAlexaboutno aff
Karl Raitz

Bibliographic record

VenueUniversity Press of Kentucky eBooks · 2020
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringArchaeologyRevenueGeographyAgricultural economicsFisheryWaste managementBusinessFinanceEconomicsBiology

Abstract

fetched live from OpenAlex

On the eve of the Civil War, Scott County farmer James M. Stone owned twenty-three enslaved people, farmed 137 acres of improved land along South Elkhorn Creek, and was one of the most prosperous farmers in the county. By 1867, his industrial distillery produced about thirty barrels of whiskey per week. He entered into a business partnership with James H. Shropshire, who assisted with management and provided some of the capital for expansion. Stone made extensive modifications to his works to comply with the new federal requirements imposed by the 1868 revenue law, including building a state-of-the-art stack-type warehouse of brick, with a metal roof and iron window shutters. Cooper Adam Michaels made barrels for Elkhorn and other distilleries. Elkhorn’s transport connections for grain, construction materials, fuel, and whiskey were unimproved roads and a track-side depot on the railroad some two and a half miles distant. Logistics proved to be problematic for the duration of Elkhorn’s operations. Elkhorn consumed more grain than was produced locally and required shipments from Outer Bluegrass counties; barley malt came from Canada, and hops arrived from brokers in Lexington and Cincinnati. Most grain was shipped in sacks. New mechanical equipment often proved unreliable or unsuited for its application, necessitating ad hoc repairs. The distillery operation included a large pen where hogs were fed slop.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.086
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.002
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0860.017

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.166
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueUniversity Press of Kentucky eBooksSame topicAmerican Environmental and Regional HistoryFrench-language works237,207